32 citations · 35 across the 7 of their papers we have counts for
5 papers · 1 filter
Is Success All You Need? Investigating the Impact of Input Perturbations on VLA Behaviour in Tabletop Manipulation Tasks
Sophie Higham, Riccardo Andrea Izzo, Matteo Matteucci +1
Vision-Language-Action (VLA) models have achieved high task success rates on robot manipulation task benchmarks. More recently, there has been an emphasis on evaluating the robustn…
Decoupled Early Exits for Task-Dependent Compute Allocation in Flow-Matching VLAs
Riccardo Andrea Izzo, Rimvydas Rubavicius, Gianluca Bardaro +3
Flow-matching Vision-Language-Action (VLA) models have emerged as a potential solution for generalist robot control, designed by combining a pretrained Vision-Language Model (VLM)…
Multimodal Behavior Tree Generation: A Small Vision-Language Model for Robot Task Planning
Cristiano Battistini, Riccardo Andrea Izzo, Gianluca Bardaro +1
Large language models have been widely used for robotic task planning, often taking advantage of representations such as Behavior Trees (BTs). Vision-Language Models (VLMs) have ex…
BTGenBot-2: Efficient Behavior Tree Generation with Small Language Models
Riccardo Andrea Izzo, Gianluca Bardaro, Matteo Matteucci
Recent advances in robot learning increasingly rely on LLM-based task planning, leveraging their ability to bridge natural language with executable actions. While prior works showc…
BTGenBot: Behavior Tree Generation for Robotic Tasks with Lightweight LLMs
Riccardo Andrea Izzo, Gianluca Bardaro, Matteo Matteucci
This paper presents a novel approach to generating behavior trees for robots using lightweight large language models (LLMs) with a maximum of 7 billion parameters. The study demons…